An Intelligent Model of Mixed Teaching of Business English in Colleges and Universities in the Information Age

Abstract

Abstract With the rise of foreign trade, various industries have increased the demand for foreign language talents, and colleges and universities have paid more attention to the intelligent teaching of business English. Based on this, this paper proposes a dynamic time regularization recognition algorithm (DTW). It improves the recognition efficiency of the algorithm, preprocesses the speech signal, and selects the feature parameter MFCC. The improved DTW algorithm is used to construct an intelligent mode of mixed teaching of business English in colleges and universities, and the learning effect of the students majoring in business English is calculated and evaluated. The research object of this paper is students majoring in business English at college C and is conducted by conducting experimental research on their oral learning, article writing ability, and learning attitude. From the results of oral teaching, the coefficient of variation P-values before and after the application of each group are 0.000, 0.002 and 0.000 respectively, which are all less than 0.05, indicating that students with different English fundamentals have a more significant effect of improving their performance after applying the mixed teaching mode. Before the application, the average score for article writing increased from 59.67 to 64.50. In terms of affective attitude and value, the learning interest risen from 2.75 to 2.97, with the most significant improvement Thus, it can be seen that the intelligent hybrid teaching mode constructed in this paper can effectively improve the teaching effect of business English in colleges and universities.